The paper proposes a single‑utterance test‑time adaptation (TTA) method for speech enhancement that uses an autoregressive prior trained on clean speech latent representations from a neural audio codec. The adaptation regularizes a pretrained enhancement model by minimizing the Kullback‑Leibler divergence between the enhanced speech distribution and the clean speech prior. Experiments on multiple noisy speech datasets demonstrate consistent improvements in speech quality, especially when training and testing noise conditions differ.
arXiv:2604. 24199v4 Announce Type: replace-cross Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem.
arXiv:2510. 20441v2 Announce Type: replace-cross Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications.
The paper adapts Reinforce Adjoint Matching (RAM) to generative speech enhancement, allowing a pretrained model to be post‑trained on real recordings using weak supervision such as text transcripts. RAM shifts the model’s conditional distribution toward higher‑reward outputs by generating enhanced speech on‑policy, evaluating each output with a potentially non‑differentiable reward, and analytically re‑noising the endpoint to create inputs for a reward‑guided regression objective. Experiments on real CHiME‑4 recordings show a 5.08‑percentage‑point reduction in word error rate compared to the pretrained FlowSE model, while maintaining all reported non‑intrusive speech quality metrics and receiving no significant preference in a subjective listening test.
The paper introduces Masked Autoregressive Speech Enhancement (MARSE), a method that iteratively decodes masked clean speech frames using continuous latent representations from a neural audio codec (DAC). Unlike prior approaches that relied on discrete token representations, MARSE employs a Conformer model and explores various decoding policies to balance speech enhancement performance with computational cost. The authors provide audio examples and code online to demonstrate the method’s effectiveness.
arXiv:2602. 20967v2 Announce Type: replace-cross Abstract: Automatic speech recognition (ASR) degrades severely in noisy environments.
Speech-based Alzheimer's disease (AD) detection increasingly relies on speech-enhanced and curated versions of the Pitt Corpus, where speech enhancement, sample selection, and demographic balancing ar...
The study examines how speech preprocessing—such as enhancement, sample selection, and demographic balancing—affects Alzheimer’s disease detection models that use the Pitt Corpus. Experiments reveal that while speech‑enhanced datasets boost in‑domain accuracy, they diminish cross‑dataset robustness and introduce class imbalance and prediction shifts, even when training and testing enhancements are matched. Large audio‑language models show similar sensitivity, indicating that cleaner speech does not guarantee better real‑world performance.
The paper addresses challenges in extracting target and multiple speakers from real conversational speech, noting that real conversations contain more silence and enrolment samples that differ from the target speech. It introduces a new loss function that reduces the impact of excess silence during training, yielding improvements in STOI (from 0.55 to 0.60) and frequency‑weighted segmental SNR (from 4.35 to 5.12). The study also investigates how mismatches between enrolment and target speech affect performance.
The study examines how the realism of synthetic room impulse response (RIR) datasets influences the training of DeepFilterNet3 for single‑channel speech enhancement. By comparing a DNS4 image‑source‑method RIR set with a higher‑fidelity hybrid wave‑based and geometrical acoustics RIR set, the authors find that the more realistic dataset consistently improves objective speech enhancement metrics and significantly reduces ASR word error rates on unseen measured RIRs. The results suggest that overall realism in synthetic acoustic training data enhances DeepFilterNet3’s generalization to new environments.
arXiv:2606. 31552v1 Announce Type: cross Abstract: Room-acoustic simulations are widely used to augment training data for deep-learning-based speech enhancement.
arXiv:2604. 01832v1 Announce Type: cross Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge.
arXiv:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.